Super-resolution X-ray imaging method and apparatus

ABSTRACT

The presently-disclosed technology improves the resolution of an x-ray microscope so as to obtain super-resolution x-ray images having resolutions beyond the maximum normal resolution of the x-ray microscope. Furthermore, the disclosed technology provides for the rapid generation of the super-resolution x-ray images and so enables real-time super-resolution x-ray imaging for purposes of defect detection, for example. A method of super-resolution x-ray imaging using a super-resolving patch classifier is provided. In addition, a method of training the super-resolving patch classifier is disclosed. Other embodiments, aspects and features are also disclosed.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional Application No.62/694,319, filed on Jul. 5, 2018. The aforementioned application ishereby incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present disclosure relates to methods and apparatus for x-rayimaging.

2. Description of the Background Art

One aspect of the present disclosure relates to the examination andinspection of objects using x-rays that have structures of interest onthe micrometer to nanometer scale. Such objects include integratedcircuits (ICs) and integrated circuit packaging, including multi-chippackages (MCPs) with silicon interposers and through-silicon vias(TSVs). Certain natural objects (crystals or quasi-crystals) orbiological structures may also be examined using these techniques.

SUMMARY

The presently-disclosed technology improves the resolution of an x-raymicroscope so as to obtain super-resolution x-ray images havingresolutions beyond the maximum normal resolution of the x-raymicroscope. Furthermore, the disclosed technology provides for the rapidgeneration of the super-resolution x-ray images and so enables real-timesuper-resolution x-ray imaging for purposes of defect detection, forexample. A method of super-resolution x-ray imaging using asuper-resolving patch classifier is provided. In addition, a method oftraining the super-resolving patch classifier is disclosed.

Other embodiments, aspects and features are also disclosed.

DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an overview in cross-section of a high-speed x-rayimaging system in accordance with a preferred embodiment of theinvention.

FIG. 1B illustrates an overview in cross-section of an imaging systembased on a conventional x-ray microscope geometry in accordance with analternate embodiment of the invention.

FIG. 2 is a diagram depicting a high-level view of super-resolutionx-ray imaging in accordance with an embodiment of the invention.

FIG. 3 is a flow chart of a method of super-resolution x-ray imaging ofan object for defect detection in accordance with an embodiment of theinvention.

FIG. 4 depicts an array of super-resolving patch classifiers inaccordance with an embodiment of the invention.

FIG. 5 depicts a high-resolution x-ray image divided into patches inaccordance with an embodiment of the invention.

FIG. 6A is a diagram depicting the generation of a super-resolutionx-ray patch from multiple high-resolution x-ray patches using a trainedsuper-resolution patch classifier (SRPC) in accordance with anembodiment of the invention.

FIG. 6B is a diagram depicting the generation of a super-resolutionx-ray patch from multiple high-resolution x-ray patches using a trainedsuper-resolving neural network in accordance with an embodiment of theinvention.

FIG. 7 depicts a super-resolution x-ray image formed fromsuper-resolution patches in accordance with an embodiment of theinvention.

FIG. 8 is a diagram depicting the generation of a super-resolution x-raypatch from corresponding high-resolution x-ray patches andnearest-neighbor patches using a trained super-resolution patchclassifier in accordance with an embodiment of the invention.

FIG. 9 is a flow chart of an exemplary method of training asuper-resolving patch classifier in accordance with an embodiment of theinvention.

FIG. 10 depicts a low-resolution x-ray image divided into patches foruse in training an SRPC in accordance with an embodiment of theinvention.

FIG. 11 is a diagram depicting the training of a super-resolution patchclassifier using multiple lower-resolution patches as inputs and acorresponding high-resolution patch as known, correct output inaccordance with an embodiment of the invention.

DETAILED DESCRIPTION

Recently, in U.S. Pat. No. 9,129,715, issued Sep. 8, 2015, Adler et al.has disclosed an innovative x-ray microscope with a high flux x-raysource that allows high speed metrology or inspection of objects such asintegrated circuits (ICs), printed circuit boards (PCBs), and other ICpackaging technologies. The object to be investigated is illuminated bycollimated, high-flux x-rays from an extended source having a designatedx-ray spectrum. The system also comprises a stage to control theposition and orientation of the object; a scintillator that absorbsx-rays and emits visible photons positioned in very close proximity to(or in contact with) the object; an optical imaging system that forms ahighly magnified, high-resolution image of the photons emitted by thescintillator; and a detector such as a CCD array to convert the image toelectronic signals.

The present disclosure provides a technology for improving theresolution of the x-ray images obtainable by a high-speed x-ray imager,such as that disclosed by Adler et al. in U.S. Pat. No. 9,129,715, or aninstrument with a conventional x-ray microscope geometry. Thepresently-disclosed technology improves the resolution beyond themaximum normal resolution of the x-ray microscope. Furthermore, thedisclosed technology provides for the rapid generation of thesuper-resolution x-ray images and so enables real-time super-resolutionx-ray imaging for purposes of defect detection.

FIG. 1A illustrates an overview in cross-section of an embodiment of ahigh-speed x-ray imaging system in accordance with a preferredembodiment of the invention. An x-ray emitter 101 emits x-rays 111.These x-rays are then shaped into a collimated x-ray beam 211, in someembodiments using distance from the emitter 101 and a plate 140 with anaperture 142. This collimated x-ray beam 211 then illuminates an object200 to be examined. The x-rays that are transmitted through the object200 illuminate a scintillator assembly 300 comprising a scintillator 310and, in some embodiments, a support 350 for the scintillator. Thescintillator 310 absorbs a portion of the x-rays and releases some ofthe energy so absorbed with the emission of visible photons 411.

Using an optical system 400, a magnified image 511 of the visiblephotons 411 emitted by the scintillator is formed on an image detector500. The image detector 500 converts the intensity of the magnifiedimage 511 to an electronic signal. The image detector 500 can comprisean electronic sensor, such as a charge-coupled device (CCD), or anotherimage sensor known to those skilled in the art. The electronic signal istransmitted through a connector 558 to a system of electronics 600 that,in some embodiments can display the image results, and in someembodiments can store the image results and/or perform image processingalgorithms on the image results in conjunction with one or more computersystems 700.

For any source emitting ionizing radiation such as x-rays, it is oftenwise to provide shielding 998 around the x-ray source 100, and in somesituations legally required for operation. Such shielding 998 can be asimple enclosure of shaped sheets of lead metal, or a more intricatedesign fabricated from any of a number of x-ray absorbing materials,such as lead-doped glass or plastic, that will be known to those skilledin the art. Shielding is desirable to keep random x-rays, eitherdirectly from the emitter 101 or reflected from some other surface, fromcausing unwanted effects, particularly spurious signals in the variouselectronic components used to control the system.

Likewise, for some embodiments, additional shielding 999 around the beampath may also be desired, and in some cases be legally required foroperation. Such additional shielding 999 can be a simple enclosure ofshaped sheets of lead metal, or a more intricate design fabricated fromany of a number of x-ray absorbing materials such as lead-doped glass orplastic, that will be known to those skilled in the art. Additionalshielding 999 is desirable to keep random x-rays, either directly fromthe emitter 101 or reflected from some other surface, from causingunwanted effects, particularly spurious signals in the variouselectronic components used to control the system.

Because certain image detectors 500 such as those comprising CCD sensorscan be particularly sensitive to x-ray exposure, in some embodiments aportion of the scintillator assembly 300 may also be fabricated in wholeor in part using a material, such as a lead-doped glass, which absorbsx-rays while transmitting the visible photons 411 emitted by thescintillator. Other embodiments comprising a system design that placesthe image sensor 510 out of the x-ray beam path, as will be disclosed inmore detail later in this application, may also be used if additionalisolation from x-rays is desired.

As depicted in FIG. 1A, the x-ray source may comprise a mount 106 thatcan move the position of the x-ray source 100 relative to the object200, thereby changing the angle of incidence of the x-ray beam on theobject. The mount 106 can be designed to allow the x-ray source 100 toswing in the x-z plane, in the y-z plane, or any other combination ofaxes. The source can also be moved along the z-axis to move the x-raysource 100 closer to the object 200. This may have the effect of makingthe beam brighter, increasing signal strength, at the cost of having anx-ray beam that is less collimated, reducing resolution. This effect maybe reduced or eliminated by reducing the spot size of the x-ray source.

Motion of the x-ray source 100 using the mount 106 may be controlled bythe computer system 700 several ways. In some embodiments, the sourcemount 106 may move the x-ray source 100 to a fixed location to allow animage to be captured. In some embodiments, the mount 106 can move thex-ray source 100 continuously as images are gathered, allowing thedynamic change of x-ray intensity as transmitted through the object 200to be recorded as a function of illumination angle. In some embodiments,the x-ray emitter 101 may be moved to at least 10 degrees off the normalincidence angle.

In some embodiments, further adjustment of the angle of incidence of thex-ray beam 211 on the object 200 may be achieved by coordinating themotion of the x-ray source 100 using the source mount 106 with themotion of the object 200 using the object mount 250. In someembodiments, the motion of the mount 250 is controlled by a controller259 through a connector 258. The controller 259 is in turn directedeither by direct input from an operator, or by electronic instructionsprovided by the computer system 700.

In some embodiments, the shielding 998 will be designed to enclose thex-ray source 100 and the source mount 106. In other embodiments, theshielding 998 can be designed to only enclose the x-ray source, with themount 106 designed to move the shielding 998 as it moves the x-raysource 100.

In some embodiments of the invention, multiple x-ray sources may be usedto produce images with different angles of incidence. The x-ray sourcesmay be fixed in space or moveable, and they may be operated sequentiallyor simultaneously. The x-ray sources may be operated manually orcontrolled by one or more computer systems 700.

As depicted in FIG. 1A, for the super-resolving capability, the computersystem 700 includes a super-resolving imaging module (SRIM) and a patchclassifier training module (PCTM). The SRIM implements a method forgenerating super-resolution x-ray images which is described below inrelation to FIG. 3. The PCTM implements a method of training asuper-resolving patch classifier, the training method being describedbelow in relation to FIG. 9.

FIG. 1B illustrates an overview in cross-section of an imaging systembased on a conventional x-ray microscope geometry in accordance with analternate embodiment of the invention. The depicted system uses pointprojection microscopy (PPM) to form images of objects, such asintegrated circuits, printed circuit boards, or other packagingstructures such as interposers. Direct shadows of objects are formedusing x-rays emitted from a small point source.

In particular, a “point” source 10 emits x-rays 11 at a wide range ofangles. The object 20 to be examined comprising detailed structures 21is placed some distance away, so that it casts an enlarged shadow 30comprising features 31 corresponding to the structures 21 on a detectionscreen 50 some distance behind the object.

The advantage to the system in FIG. 1B is its simplicity—it is a simpleshadow projection, and the magnification can be increased by simplyplacing the detector farther away. By not using inefficient zone plates,higher intensity and therefore faster image collection times areachieved. For an object of infinite thinness and with no internalstructure, this may be adequate. Unfortunately, integrated circuits andpackaging materials are not infinitely thin; they have complex 3Dstructures, and the wide angular range of the shadow projection systemmeans that identical features illuminated at an angle cast verydifferent shadows from those same features illuminated head on. Thisparallax error, illustrated in FIG. 1B, must be taken into account inthe image analysis of any shadow projection system, and prevents itseasy use in an inspection system, since pixel-by-pixel comparison isimpossible for images taken with different illumination angles.Resolution is also an issue with PPM systems. Although x-ray wavelengthscan be chosen to be short enough that significant diffraction does notoccur, blurring is still a significant problem. The “point” source isactually the spot where an electron beam collides with an anode, and atypical x-ray source spot is at least 1 micron in diameter. Theresolution of the shadow is therefore limited by the size of theoriginal source spot, and at some distance, the shadows from an extendedsource will blur.

In an alternate embodiment, the technique for performingsuper-resolution x-ray imaging may be used with the such a PPM system asdepicted in FIG. 1B. As depicted, for the super-resolving capability,the system includes a computer system 700 which includes a SRIM and aPCTM. The SRIM implements a method for generating super-resolution x-rayimages which is described below in relation to FIG. 3. The PCTMimplements a method of training a super-resolving patch classifier, thetraining method being described below in relation to FIG. 9.

In addition, for the super-resolving capability, the system in FIG. 1Bmay further include a detector position controller 60 and a detectorposition actuator 62. The computer system 700 is communicatively coupledto the detector position controller 60 which controls the lateralpositioning of the detector (detection screen) 50 using the actuator 62.Alternately, or in addition, the system in FIG. 1B may further includean object position controller 70 and a object position drive 72. Thecomputer system 700 is communicatively coupled to the detector positioncontroller 70 which controls the lateral positioning of a stage holdingthe object 20 using the actuator 72.

FIG. 2 is a diagram depicting a high-level view of super-resolutionx-ray imaging in accordance with an embodiment of the invention. A setof N high-resolution x-ray images (HRXI-1, HRXI-2, . . . , HRXI-N) areobtained from a same object which is being imaged. However, the imagingconditions of each of the N x-ray images in the set are different. Forexample, the angle of incidence of the x-ray beam may be differentbetween the x-ray images.

Subsequently, processing is performed on the set of N high-resolutionx-ray images to generate a super-resolution x-ray image (SRXI) of theobject. The processing performed uses a trained super-resolution patchclassifier (SRPC) and is described in detail herein.

FIG. 3 is a flow chart of a method 1000 of super-resolution x-rayimaging of an object for defect detection in accordance with anembodiment of the invention. The method 1000 may be performed, forexample, under control of a super-resolution imaging module (SRIM) whichmay be executed, for example, by the computer system 700 in FIG. 1A orFIG. 1B.

The method 1000 as depicted includes two preliminary steps (1002 and1004). These preliminary steps are not necessary to be performed againif they have been previously performed for the object type.

The first preliminary step involves the training 1002 of asuper-resolution patch classifier (SRPC) for the object type. Forexample, if the object is a type of semiconductor package, then the SRPCmay be trained with samples of that type of semiconductor package. Asanother example, if the object is a type of printed circuit board, thenthe SRPC may be trained with samples of that type of printed circuitboard. An exemplary method 1100 for implementing the training 1002 ofthe SRPC for the object type is described below in relation to FIG. 9.The method 1100 may be performed, for example, under control of a patchclassifier training module (PCTM) which may be executed, for example, bythe computer system 700 in FIG. 1A or 1B.

The second preliminary step involves replicating 1004 the trained SRPCto form an array of trained SRPC instances, an example of which isdepicted in FIG. 4. The array of trained SRPC instances may beimplemented using a parallel computing platform, such as that provided,for example, by graphic processing units of NVIDIA Corporation of SantaClara, Calif.

The number of patches is preferably a multiple of the number of trainedSRPC instances. For maximum parallelization, the numbers will be equal.On a particular computing platform [i.e. a graphics processing unit(GPU) based system or a central processing unit (CPU) based system), thesystem may be optimized for maximal throughput.

Subsequently to the preliminary steps 1002 and 1004, the steps 1006 to1028 may be used to perform imaging of one or more objects of the sametype as that used to train the SRPC in step 1002.

Per step 1006, the object to be imaged is positioned in an x-ray imager,such as the high-speed x-ray imager described above in relation to FIG.1A, or such as the x-ray microscope based imager which is describedabove in relation to FIG. 1B. The object to be imaged in the high-speedx-ray imager of FIG. 1A is shown as the object 200, and the object to beimaged in the x-ray microscope based imager of FIG. 1B is shown as thetest object 20.

Per step 1010, an imaging condition for the x-ray imager is set. In anexemplary implementation which uses the high-speed x-ray imager of FIG.1A, the imaging condition may be set by setting the angle of incidenceof the x-rays onto the surface of the object. In an alternateimplementation which uses the x-ray microscope geometry of FIG. 1B, theimaging condition may be set by controlling a detector position or anobject position. In the former case, the detector may be moved by asub-pixel distance, while the source and object remain fixed. In thelatter case, the object (i.e. the sample or the target) may be moved bya sub-pixel amount, while leaving the source and detector fixed.

In the implementation that changes the angle of incidence, the angle ofincidence may be set by controlling the source position. Note that thegeometry of the x-ray imager described in relation to FIG. 1A is suchthat a small, but controllable, change in the position of the x-raysource (so as to change the incident angle) results in a much smallerchange in the landing position of the incident x-ray beam. The change inthe landing position may be approximately two orders of magnitude (i.e.a hundred times) smaller than the change in the source position. Thisenables the very small changes (sub-pixel shifts) in the x-ray beamlanding position to be consistently made by controlling the position ofthe x-ray source.

In the implementation that changes the detector position, the detectorposition may be set by controlling a lateral position of the detector intwo dimensions. The lateral position may be controlled in a finely tunedmanner so as to shift the detector position by sub-pixel distances.

Similarly, in the implementation that changes the object position, thedetector position may be set by controlling a lateral position of theobject in two dimensions. The lateral position may be controlled in avery finely tuned manner so as to shift the object position by sub-pixeldistances (where the pixel distance is smaller at the object than at thedetector due to the geometry of the apparatus).

Per step 1012, a high-resolution x-ray image of the object is obtained.In an exemplary implementation, this x-ray image may be obtained at amaximum or near maximum resolution setting of the high-speed x-rayimager. For instance, the high-resolution x-ray image may have aresolution of one micron.

Per step 1014, a determination may be made as to whether there arefurther x-ray images to be obtained so as to complete a set of Nhigh-resolution x-ray images. For example, the set of high-resolutionx-ray images obtained may have N=3 (or N=4, or N=5, . . . ) differentx-ray images therein. (The number of x-ray images in the set must be twoor more.) More x-ray images in the set will result in greater resolutionand/or greater accuracy achievable by the presently-disclosedsuper-resolving technology. The trade-off being that more x-ray imagesin the set generally require more time to obtain and more resources toprocess. The images are collected in a way such the corresponding changein incident angle (using high-speed x-ray imager of FIG. 1A), ordetector position or object position (using x-ray microscope of FIG. 1B)results in sub-pixel shifts between the images.

If there are more x-ray images to be obtained to complete the set of Nhigh-resolution x-ray images, then the method 1000 may loop back toperform step 1010. Otherwise, if the set of N high-resolution x-rayimages is complete, then the method 1000 may move forward to step 1020.

Per step 1020, each HR x-ray image being divided into patches, thepatches associated with a same patch region (i.e. a same patch position)are input into a corresponding trained SRPC instance. FIG. 5 depicts asingle HR x-ray image being divided into patches, while correspondingSRPC instances are shown in FIG. 4. The inputting of the patches fromthe multiple HR x-ray images into the corresponding trained SRPCinstance may be done in parallel for all patch regions (i.e. for allpatch positions).

Per step 1022, each trained SRPC instance outputs a super-resolution(SR) patch based on the set of HR patches which were input into thatSRPC instance. This step is illustrated in FIGS. 6A and 6B.

As shown in FIG. 6A, the trained SRPC instance receives as inputs a setof N HR patches (HRP-1, HRP-2, . . . , HRP-N). Each HR patch is from adifferent HR image and corresponds to a same patch region in the HRimage.

In the illustrated example, each HR patch has dimensions of 2×2 pixels.In other implementations, each HR patch may have other dimensions, suchas for example, 3×3 pixels, 4×4 pixels, or other rectangular dimensions(which need not be square). Each pixel has a multiple bit value. Forexample, the multiple bit value may be an 8-bit value, 10-bit value,12-bit value, 16-bit value, or values of other numbers of bits. In thefigure, the four pixels of HRP-1 are labeled 00-1, 01-1, 10-1 and 11-1;the four pixels of HRP-2 are labeled 00-2, 01-2, 10-2 and 11-2; . . . ;and the four pixels of HRP-N are labeled 00-N, 01-N, 10-N and 11-N.

The trained SRPC instance outputs a SR patch (SRP) that is based on theset of N input HR patches of the same patch region. The SRP that isoutput is a super resolution version of that same patch region. In theillustrated example, the SR patch has dimensions of 4×4 pixels (labeled00, 01, 02, 03, 10, 11, 12, 13, 20, 21, 22, 23, 30, 31, 32 and 33), andeach pixel in the SR patch may have a value with the same number of bitsas each pixel in the HR patch. In this case, the resolution of the patchregion is improved by a factor of 4/2=2 (in each dimension). In otherimplementations, the SR patch may improve the resolution by otherfactors, which need not be integers (so long as the factor is greaterthan one, of course). For example, if the SR patch in FIG. 6A haddimensions of 3×3 pixels (instead of 4×4 pixels), then the resolutionwould be improved by a factor of 3/2=1.5 (in each dimension). In yetother implementations, each pixel in the SR patch may have a value withthe different number of bits as each pixel in the HR patch.

FIG. 6B differs from FIG. 6A in that the SRPC instance is specificallyimplemented as an instance of a trained super-resolving neural network(SRNN). In an exemplary implementation, the super-resolving neuralnetwork may implemented as a deep convolutional network. An example ofsuch a convolutional network is described by Chao Dong et al. in “SuperResolution Using Deep Convolutional Networks,” IEEE Transactions onPattern Analysis and Machine Intelligence, Vol. 38, Issue 2, Feb. 1,2016, the disclosure of which is hereby incorporated by reference.

Other types of regression-based classifiers besides neural networks mayalso be used to implement the super-resolving patch classifier. Oneexample of a different type of regression-based classifier that may beused is a support vector machine.

Per step 1024, the super-resolution patches (SRPs) for all the patchregions of an image are combined or “stitched together” to form asuper-resolution x-ray image (SRXI) of the object. This step isillustrated in FIG. 7. Advantageously, the SRXI of the object may have ahigher resolution than the maximum resolution normally possible with thex-ray imager being used. In particular, resolutions that are a fractionof a micron are achievable using this super-resolving technique.

Per step 1026, the super-resolution x-ray image of the object may beused for a practical purpose. For example, consider an object that is amanufactured object, such as a packaged semiconductor, or a printedcircuit board, or an interposer, or other manufactured substrate ordevice. In one use case, the super-resolution x-ray image of such amanufactured object may be displayed on a monitor for viewing by anoperator. As another use case, the super-resolution x-ray image may beused for defect detection, which may be performed in an automatedmanner. As another use case, the super-resolution x-ray image may beused to monitor the manufacturing process.

Per step 1028, a determination may be made as to whether there isanother object of same type to image with this super-resolving x-rayimaging technique. If so, then the method 1000 loops back to step 1006in which the next object to be imaged is positioned in the x-ray imager.Otherwise, if there are no more objects of the same type to be imaged,then method 1000 is done.

While the FIGS. 6A and 6B illustrate an example where the pixel data ofthe corresponding high-resolution patches themselves are usedexclusively as the inputs to the super-resolving patch classifier. Otherimplementations may input further information beyond the high-resolutionpatches themselves. For example, the pixel data from neighboringhigher-resolution patches may also be utilized.

FIG. 8 is a diagram depicting the generation of a super-resolution x-raypatch from corresponding high-resolution x-ray patches andnearest-neighbor patches using a trained super-resolution patchclassifier in accordance with an embodiment of the invention. Asdepicted, the input to the SRPC includes not only the pixel data of thecorresponding high-resolution patches (HRP-1, HRP-2, . . . , HRP-N) butalso the pixel data of the four nearest-neighbor high-resolution patchesto each high-resolution patch. The four nearest-neighbor patches includethe up-neighbor high-resolution patches (HRP.U-1, HRP.U-2, . . . ,HRP.U-N), the down-neighbor high-resolution patches (HRP.D-1, HRP.D-2, .. . , HRP.D-N), the left-neighbor high-resolution patches (HRP.L-1,HRP.L-2, . . . , HRP.L-N), and the right-neighbor high-resolutionpatches (HRP.R-1, HRP.R-2, . . . , HRP.R-N).

For high-resolution patches at the edge of an image frame, theinformation that it is positioned at an edge may be indicated, forexample, by there being no pixel data for the nearest-neighbor patchbeyond the edge. For high-resolution patches at the corner of an imageframe, the information that it is positioned at a corner may beindicated, for example, by there being no pixel data for thenearest-neighbor patches beyond the two corner edges.

FIG. 9 depicts an exemplary method 1100 of training a super-resolvingpatch classifier in accordance with an embodiment of the invention. Thismethod 1100 may be used to implement the training step 1002 in FIG. 3.The method 1100 may be performed, for example, under control of a patchclassifier training module (PCTM) which may be executed, for example, bythe computer system 700 in FIG. 1A or FIG. 1B.

In step 1102, an object sample is positioned in the high-speed x-rayimager. The object sample may be the same type of object that will besuper-resolution x-ray imaged by the method 1000 of FIG. 3. For example,if the objects to be imaged are a certain type of packagedsemiconductor, then the object sample may be of the same type ofpackaged semiconductor.

In step 1104, a high-resolution x-ray image of the object sample may beobtained. The high-resolution x-ray image may be obtained at the highestresolution setting, or a setting near the highest resolution setting, ofthe x-ray imager. In this training method 1100, the high-resolutionx-ray image is not used as an input to the SRPC; instead, thehigh-resolution x-ray image is used as a “correct” output of the SRPCfor the purpose of training the SRPC.

In step 1110, the imaging condition for the x-ray imager is set. In anexemplary implementation, the imaging condition may be set by settingthe angle of incidence of the x-rays onto the surface of the object. Forexample, the angle of incidence may be set by controlling the sourceposition using, for example, the mount 106 to move the source 100 in thehigh-speed x-ray imager of FIG. 1A. In an alternate implementation, theimaging condition may be set by setting a lateral position of thedetector using, for example, the controller 60 and driver 62 in theapparatus of FIG. 1B. In another alternate implementation, the imagingcondition may be set by setting a lateral position of the object beingimaged using, for example, the controller 70 and driver 72 in theapparatus of FIG. 1B.

Per step 1112, a lower-resolution x-ray image of the object is obtained.In an exemplary implementation, the previously-obtained high-resolutionx-ray image in step 1104 is of a resolution that is higher by a specificfactor than the resolution of the lower-resolution image obtained inthis step. This specific factor is the same as the factor in FIG. 6Athat the resolution of the super-resolution patch output by the SRPC ishigher than the resolution of the high-resolution patches input to theSRPC.

For example, if the high-resolution patches have resolution of 1 micronand the super-resolution patches have a resolution of 0.5 microns forsuper-resolution x-ray imaging, then the specific factor is 2. As such,in this case, the high-resolution x-ray image obtained in step 1104would be at a resolution that is 2 times (in each dimension) theresolution of the lower-resolution x-ray image obtained in step 1112.For example, the high-resolution x-ray image obtained in step 1104 maybe at a resolution of 1 micron, and the lower-resolution obtained instep 1112 may be at a resolution of 2 microns.

Per step 1114, a determination may be made as to whether there arefurther x-ray images to be obtained so as to complete the set of Nlower-resolution x-ray images. If there are more x-ray images to beobtained to complete the set of N lower-resolution x-ray images, thenthe method 1100 may loop back to perform step 1110. Otherwise, if theset of N lower-resolution x-ray images is complete, then the method 1100may move forward to step 1116.

Per step 1116, each LR x-ray image being divided into patches asillustrated in FIG. 10, the LR patches associated with a same patchregion (i.e. a same patch position) are used as input into the SRPCbeing trained, where the known correct output is the corresponding HRpatch of the HR x-ray image obtained in step 1104. This training step isillustrated in FIG. 11. As shown in FIG. 11, the training of asuper-resolution patch classifier uses multiple lower-resolution patchesas inputs and a corresponding high-resolution patch as a known, correctoutput in accordance with an embodiment of the invention. This trainingstep shown in FIG. 11 may be repeated for all patch regions (i.e. forall patch positions).

Further object samples of the same may be used for training the SRPC.Per step 1118, if there is another object sample to be used fortraining, then the method 1100 loops back to step 1102. Otherwise, thetraining is complete, and the trained SRPC is obtained per step 1120.

CONCLUSION

In the present disclosure, numerous specific details are provided, suchas examples of systems, components, and methods, to provide a thoroughunderstanding of embodiments of the invention. Persons of ordinary skillin the art will recognize, however, that the invention can be practicedwithout one or more of the specific details. In other instances,well-known details are not shown or described to avoid obscuring aspectsof the invention.

While specific embodiments of the present invention have been provided,it is to be understood that these embodiments are for illustrationpurposes and not limiting. Many additional embodiments will be apparentto persons of ordinary skill in the art reading this disclosure.

What is claimed is:
 1. A method of super-resolution x-ray imaging, themethod comprising: obtaining a set of high-resolution x-ray images of anobject; dividing each of the high-resolution x-ray images in the setinto high-resolution patches; for each patch region, inputting thehigh-resolution patches corresponding to the patch region to an instanceof a trained super-resolving patch classifier to generate asuper-resolution patch for the patch region; and stitching together thesuper-resolution patches for the patch regions to obtain asuper-resolution x-ray image of the object.
 2. The method of claim 1,wherein the set of high-resolution x-ray images of the object areobtained by changing an imaging condition; acquiring a high-resolutionx-ray image using the imaging condition; and repeating the changing andacquiring steps until the set of high-resolution x-ray images iscomplete.
 3. The method of claim 2, wherein changing the imagingcondition results in a sub-pixel shift from one high-resolution x-rayimage to a next high-resolution x-ray image in the set.
 4. The method ofclaim 3, wherein the imaging condition comprises an incident angle ofthe x-ray beam.
 5. The method of claim 4, wherein the incident angle ischanged by varying a position of an x-ray source.
 6. The method of claim3, wherein the imaging condition comprises a position of an x-raydetector.
 7. The method of claim 3, wherein the imaging conditioncomprises a position of the object.
 8. The method of claim 1, whereinthe super-resolving patch classifier comprises a regression classifier.9. The method of claim 8, wherein the regression classifier comprises anartificial neural network.
 10. The method of claim 8, wherein theregression classifier comprises a support vector machine.
 11. The methodof claim 1, further comprising: displaying the super-resolution x-rayimage on a monitor.
 12. The method of claim 1, further comprising: usingthe super-resolution x-ray image for detecting defects in the object.13. The method of claim 1, wherein the super-resolution x-ray image isused for process monitoring.
 14. The method of claim 1, wherein thesuper-resolution x-ray image has a resolution that is smaller in sizethan the high-resolution x-ray image by at least a factor of two.
 15. Anapparatus for super-resolution x-ray imaging of an object, the apparatuscomprising: an x-ray imager; and a computer system for controlling thex-ray imager, wherein the computer system comprises a super-resolvingimaging module, the super-resolution imaging module performing stepsincluding: obtaining a set of high-resolution x-ray images of theobject; dividing each of the high-resolution x-ray images in the setinto high-resolution patches; for each patch region, inputting thehigh-resolution patches corresponding to the patch region to an instanceof a trained super-resolving patch classifier to generate asuper-resolution patch for the patch region; and stitching together thesuper-resolution patches for the patch regions to obtain asuper-resolution x-ray image of the object.
 16. The apparatus of claim15, wherein the set of high-resolution x-ray images of the object areobtained by the super-resolution imaging module performing steps of:changing an imaging condition; acquiring a high-resolution x-ray imageusing the imaging condition; and repeating the changing and acquiringsteps until the set of high-resolution x-ray images is complete.
 17. Theapparatus of claim 16, wherein the imaging condition comprises anincident angle of the x-ray beam.
 18. The apparatus of claim 17, whereinthe super-resolution imaging module changes the incident angle byvarying a position of an x-ray source.
 19. The apparatus of claim 16,wherein the imaging condition comprises a position of an x-ray detector.20. The apparatus of claim 16, wherein the imaging condition comprises aposition of the object.
 21. The apparatus of claim 15, wherein thesuper-resolving patch classifier comprises a regression classifier. 22.The apparatus of claim 21, wherein the regression classifier comprisesan artificial neural network.
 23. The apparatus of claim 21, wherein theregression classifier comprises a support vector machine.
 24. Theapparatus of claim 15, wherein the apparatus displays thesuper-resolution image on a monitor.
 25. The apparatus of claim 15,wherein the apparatus comprises an inspection machine that uses thesuper-resolution x-ray image for detecting defects in the object. 26.The apparatus of claim 15, wherein the super-resolution x-ray image isused for process monitoring.
 27. The apparatus of claim 15, wherein thesuper-resolution x-ray image has a resolution that is smaller in sizethan the high-resolution x-ray image by at least a factor of two.